arXiv:2508.00554v4 Announce Type: replace-cross Abstract: In financial trading, large language model (LLM)-based agents demonstrate significant potential, but their decisions can be sensitive to noisy and non-stationary market information. We propose ContestTrade, a multi-agent trading system with an internal competitive mechanism inspired by institutional investment workflows. The system consists of two specialized teams: (1) a Data Team that processes and condenses massive market data into diversified textual factors optimized for constrained LLM context windows, and (2) a Research Team that
The rapid advancement of large language models and the increasing complexity of financial markets are driving the need for more sophisticated AI-driven trading systems that can handle noisy and non-stationary data.
This development indicates a significant step forward in applying multi-agent AI systems to high-stakes financial trading, potentially leading to more efficient and autonomous market operations.
The financial industry's approach to market data processing and trading decision-making is evolving from human-dominated or simpler algorithmic methods to sophisticated, competitive multi-agent AI systems.
- · AI/ML developers
- · Financial institutions adopting advanced AI
- · Quantitative trading firms
- · Traditional human traders
- · Financial institutions slow to adopt AI
- · Legacy trading platforms
Increased prevalence of AI agents actively managing financial portfolios and executing trades.
Greater market efficiency but also potentially new forms of systemic risk stemming from AI-AI interactions and emergent behaviors.
Reconfigures the role of human oversight in financial markets, shifting focus from direct execution to strategic AI management and risk mitigation.
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